{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "loose-irrigation",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "altered-desert",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_values = []; y_values = []; targets = []\n",
    "\n",
    "for i in range(0, 4):\n",
    "    \n",
    "    if i == 0:\n",
    "        sign_x = 1; sign_y = 1\n",
    "        target = 1\n",
    "    elif i == 1:\n",
    "        sign_x = -1; sign_y = 1\n",
    "        target = 0\n",
    "    elif i == 2:\n",
    "        sign_x = -1; sign_y = -1\n",
    "        target = 1\n",
    "    elif i == 3:\n",
    "        sign_x = 1; sign_y = -1\n",
    "        target = 0\n",
    "    \n",
    "    for j in range(0, 100):\n",
    "        value_x = (5 + random.randint(0, 100)) * sign_x\n",
    "        value_y = (5 + random.randint(0, 100)) * sign_y\n",
    "        \n",
    "        x_values.append(value_x)\n",
    "        y_values.append(value_y)\n",
    "        targets.append(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "asian-edition",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(x_values, columns = [\"x\"])\n",
    "df[\"y\"] = y_values\n",
    "df[\"Decision\"] = targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "pleasant-content",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for target in df[\"Decision\"].unique():\n",
    "    sub_df = df[df[\"Decision\"] == target]\n",
    "    \n",
    "    if target == 1:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'orange'\n",
    "    \n",
    "    plt.scatter(sub_df['x'], sub_df['y'], c = color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "concerned-continuity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x   y  Decision\n",
       "0   37  14         1\n",
       "1  104  99         1\n",
       "2   22  91         1\n",
       "3    7  30         1\n",
       "4   20  88         1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "spectacular-richards",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"xor.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "allied-universal",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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